Assessing the Effects of Open Models of Learning and Enjoyment in a Digital Learning Game

• Abuhamdeh, S., & Csikszentmihalyi, M. (2012). The importance of challenge for the enjoyment of intrinsically motivated, goal-directed activities. Personality and Social Psychology Bulletin, 38(3), 317–330.

  Article  Google Scholar 

• Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.

  Article  Google Scholar 

• Aleven, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (2016). Instruction based on adaptive learning technologies. Handbook of Research on Learning and Instruction, 522–560.

• Anderman, E. M., & Dawson, H. (2011). Learning with motivation. Handbook of Research on Learning and Instruction, 219214.

• Ang, D., & Mitchell, A. (2019). Representation and frequency of player choice in player-oriented dynamic difficulty adjustment systems. Proceedings of the Annual Symposium on Computer-Human Interaction in Play, 589–600.

• Annetta, L. A., Minogue, J., Holmes, S. Y., & Cheng, M.-T. (2009). Investigating the impact of video games on high school students’ engagement and learning about genetics. Computers & Education, 53(1), 74–85.

  Article  Google Scholar 

• Baldwin, A., Johnson, D., & Wyeth, P. (2016). Crowd-pleaser: Player perspectives of multiplayer dynamic difficulty adjustment in video games. Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play, 326–337.

• Bateman, C., Lowenhaupt, R., & Nacke, L. E. (2011). Player typology in theory and practice. DiGRA Conference.

• Ben-Eliyahu, A., Moore, D., Dorph, R., & Schunn, C. D. (2018). Investigating the multidimensionality of engagement: Affective, behavioral, and cognitive engagement across science activities and contexts. Contemporary Educational Psychology, 53, 87–105.

  Article  Google Scholar 

• Bodily, R., Kay, J., Aleven, V., Jivet, I., Davis, D., Xhakaj, F., & Verbert, K. (2018). Open learner models and learning analytics dashboards: A systematic review. Proceedings of the 8th international Conference on learning analytics and knowledge, 41–50.

• Bodily, R., & Verbert, K. (2017). Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Transactions on Learning Technologies, 10(4), 405–418.

  Article  Google Scholar 

• Boggiano, A. K., Main, D. S., & Katz, P. A. (1988). Children’s preference for challenge: The role of perceived competence and control. Journal of Personality and Social Psychology, 54(1), 134–141.

  Article  Google Scholar 

• Bosch, N., D’Mello, S., Baker, R., Ocumpaugh, J., Shute, V., Ventura, M., Wang, L., & Zhao, W. (2015). Automatic detection of learning-centered affective states in the wild. Proceedings of the 20th international Conference on intelligent user interfaces, 379–388.

• Botelho, A. F., Baker, R. S., & Heffernan, N. T. (2017). Improving sensor-free affect detection using deep learning. International Conference on artificial intelligence in education, 40–51.

• Brockmyer, J. H., Fox, C. M., Curtiss, K. A., McBroom, E., Burkhart, K. M., & Pidruzny, J. N. (2009). The development of the game engagement questionnaire: A measure of engagement in video game-playing. Journal of Experimental Social Psychology, 45(4), 624–634.

  Article  Google Scholar 

• Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1–2), 87–110.

  Article  MATH  Google Scholar 

• Brusilovsky, P., Hsiao, I.-H., & Folajimi, Y. (2011). QuizMap: Open social student modeling and adaptive navigation support with TreeMaps. European Conference on Technology Enhanced Learning, 71–82.

• Bull, S. (2020). There are open learner models about! IEEE Transactions on Learning Technologies., 13, 425–448.

  Article  Google Scholar 

• Bull, S., Ginon, B., Boscolo, C., & Johnson, M. (2016). Introduction of learning visualisations and metacognitive support in a persuadable open learner model. Proceedings of the sixth international Conference on Learning Analytics & Knowledge, 30–39.

• Bull, S., & Kay, J. (2010). Open learner models. In Advances in intelligent tutoring systems (pp. 301–322). Springer.

• Bull, S., & Kay, J. (2008). Metacognition and open learner models. The 3rd Workshop on Meta-Cognition and Self-Regulated Learning in Educational Technologies, at ITS2008, 7–20.

• Bunian, S., Canossa, A., Colvin, R., & El-Nasr, M. S. (2018). Modeling individual differences in game behavior using HMM. ArXiv Preprint ArXiv:1804.00245.

• Burgers, C., Eden, A., van Engelenburg, M. D., & Buningh, S. (2015). How feedback boosts motivation and play in a brain-training game. Computers in Human Behavior, 48, 94–103.

  Article  Google Scholar 

• Busch, M., Mattheiss, E., Orji, R., Marczewski, A., Hochleitner, W., Lankes, M., Nacke, L. E., & Tscheligi, M. (2015). Personalization in serious and persuasive games and gamified interactions. Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play, 811–816.

• Cajimat, R. T., Errabo, D. D. R., Cascolan, H. M. S., & Prudente, M. S. (2020). Cause analysis utilizing e-assessment on the least mastered contents of K-12 basic education curriculum. Proceedings of the 2020 11th international Conference on E-education, E-business, E-management, and E-learning, 199–203.

• Carpenter, S. K. (2014). Spacing and interleaving of study and practice. Applying the Science of Learning in Education: Infusing Psychological Science into the Curriculum, 131–141.

• Carvalho, P. F., & Goldstone, R. L. (2019). When does interleaving practice improve learning?

• Charsky, D., & Ressler, W. (2011). “Games are made for fun”: Lessons on the effects of concept maps in the classroom use of computer games. Computers & Education, 56(3), 604–615.

  Article  Google Scholar 

• Chen, Z.-H., Chou, C.-Y., Deng, Y.-C., & Chan, T.-W. (2007). Active open learner models as animal companions: Motivating children to learn through interacting with my-pet and our-pet. International Journal of Artificial Intelligence in Education, 17(2), 145–167.

  Google Scholar 

• Chen, Z.-H., Liao, C., Chien, T.-C., & Chan, T.-W. (2011). Animal companions: Fostering children’s effort-making by nurturing virtual pets. British Journal of Educational Technology, 42(1), 166–180.

  Article  Google Scholar 

• Chi, M. T., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145–182.

  Article  Google Scholar 

• Chi, M. T., De Leeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477.

  Google Scholar 

• Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278.

  Article  Google Scholar 

• Croxton, D., & Kortemeyer, G. (2017). Informal physics learning from video games: A case study using gameplay videos. Physics Education, 53(1), 015012.

• Cruz-Benito, J., Sánchez-Prieto, J. C., Therón, R., & García-Peñalvo, F. J. (2019). Measuring students’ acceptance to AI-driven assessment in eLearning: Proposing a first TAM-based research model. International Conference on human-computer interaction, 15–25.

• Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience (Vol. 1990). Harper & row New York.

• Cut the Rope. (2010). ZeptoLab. https://en.wikipedia.org/wiki/Cut_the_Rope

• Baker, R. S., Gowda, S. M., Wixon, M., Kalka, J., Wagner, A. Z., Salvi, A., Aleven, V., Kusbit, G. W., Ocumpaugh, J., & Rossi, L. (2012). Towards sensor-free affect detection in cognitive tutor algebra. International Educational Data Mining Society.

• Dascalu, M.-I., Bodea, C.-N., Mihailescu, M. N., Tanase, E. A., & Ordoñez de Pablos, P. (2016). Educational recommender systems and their application in lifelong learning. Behaviour & Information Technology, 35(4), 290–297.

  Article  Google Scholar 

• Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3–4), 325–346.

  Article  Google Scholar 

• DeFalco, J. A., Rowe, J. P., Paquette, L., Georgoulas-Sherry, V., Brawner, K., Mott, B. W., Baker, R. S., & Lester, J. C. (2018). Detecting and addressing frustration in a serious game for military training. International Journal of Artificial Intelligence in Education, 28(2), 152–193.

  Article  Google Scholar 

• Deterding, S. (2016). Contextual autonomy support in video game play: A grounded theory. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 3931–3943.

• Dondlinger, M. J. (2007). Educational video game design: A review of the literature. Journal of Applied Educational Technology, 4(1), 21–31.

  Google Scholar 

• Eagle, M., Corbett, A., Stamper, J., McLaren, B. M., Baker, R., Wagner, A., MacLaren, B., & Mitchell, A. (2016). Predicting individual differences for learner modeling in intelligent tutors from previous learner activities. Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, 55–63.

• Elliot, A. J., & Murayama, K. (2008). On the measurement of achievement goals: Critique, illustration, and application. Journal of Educational Psychology, 100(3), 613–628.

  Article  Google Scholar 

• Erhel, S., & Jamet, E. (2013). Digital game-based learning: Impact of instructions and feedback on motivation and learning effectiveness. Computers & Education, 67, 156–167.

  Article  Google Scholar 

• Foster, N. L., Mueller, M. L., Was, C., Rawson, K. A., & Dunlosky, J. (2019). Why does interleaving improve math learning? The contributions of discriminative contrast and distributed practice. Memory & Cognition, 47(6), 1088–1101.

  Article  Google Scholar 

• Frommel, J., Fischbach, F., Rogers, K., & Weber, M. (2018). Emotion-based dynamic difficulty adjustment using parameterized difficulty and self-reports of emotion. Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play, 163–171.

• Fu, F.-L., Su, R.-C., & Yu, S.-C. (2009). EGameFlow: A scale to measure learners’ enjoyment of e-learning games. Computers & Education, 52(1), 101–112.

  Article  Google Scholar 

• Gee, J. P. (2003). What video games have to teach us about learning and literacy. Computers in Entertainment (CIE), 1(1), 20–20.

  Article  MathSciNet  Google Scholar 

• Giannakos, M. N. (2013). Enjoy and learn with educational games: Examining factors affecting learning performance. Computers & Education, 68, 429–439.

  Article  Google Scholar 

• Greipl, S., Ninaus, M., Bauer, D., Kiili, K., & Moeller, K. (2018). A fun-accuracy trade-off in game-based learning. International Conference on games and learning Alliance, 167–177.

• Guerra, J., Hosseini, R., Somyurek, S., & Brusilovsky, P. (2016). An intelligent interface for learning content: Combining an open learner model and social comparison to support self-regulated learning and engagement. Proceedings of the 21st international Conference on intelligent user interfaces, 152–163.

• Habgood, M. J., & Ainsworth, S. E. (2011). Motivating children to learn effectively: Exploring the value of intrinsic integration in educational games. The Journal of the Learning Sciences, 20(2), 169–206.

  Article  Google Scholar 

• Hagelback, J., & Johansson, S. J. (2009). Measuring player experience on runtime dynamic difficulty scaling in an RTS game. 2009 IEEE Symposium on Computational Intelligence and Games, 46–52.

• Hamari, J., & Tuunanen, J. (2014). Player types: A meta-synthesis.

• Harpstead, E., Richey, J. E., Nguyen, H., & McLaren, B. M. (2019). Exploring the subtleties of agency and indirect control in digital learning games. Proceedings of the 9th international Conference on Learning Analytics & Knowledge, 121–129.

• Harpstead, E., Zimmermann, T., Nagapan, N., Guajardo, J. J., Cooper, R., Solberg, T., & Greenawalt, D. (2015). What drives people: Creating engagement profiles of players from game log data. Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play, 369–379.

• Hayes, A. F., & Rockwood, N. J. (2017). Regression-based statistical mediation and moderation analysis in clinical research: Observations, recommendations, and implementation. Behaviour Research and Therapy, 98, 39–57.

  Article  Google Scholar 

• Herlocker, J. L., Konstan, J. A., & Riedl, J. (2000). Explaining collaborative filtering recommendations. Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, 241–250.

• Hooshyar, D., Yousefi, M., & Lim, H. (2018). Data-driven approaches to game player modeling: A systematic literature review. ACM Computing Surveys (CSUR), 50(6), 1–19.

  Article  Google Scholar 

• Hu, N., Zhang, J., & Pavlou, P. A. (2009). Overcoming the J-shaped distribution of product reviews. Communications of the ACM, 52(10), 144–147.

  Article  Google Scholar 

• Hummel, H. G., Van Den Berg, B., Berlanga, A. J., Drachsler, H., Janssen, J., Nadolski, R., & Koper, R. (2007). Combining social-based and information-based approaches for personalised recommendation on sequencing learning activities. International Journal of Learning Technology, 3(2), 152–168.

  Article  Google Scholar 

• Isotani, S., McLaren, B. M., & Altman, M. (2010). Towards intelligent tutoring with erroneous examples: A taxonomy of decimal misconceptions. International Conference on intelligent tutoring systems, 346–348.

• Jasin, H., Othman, M., Zain, N. M., & Osman, M. N. (2017). Proposed framework for combining Gamification elements with open learner model in a collaborative e-learning system for programming course. Computing Research & Innovation (CRINN) Vol 2, October 2017, 377.

• Jivet, I., Scheffel, M., Specht, M., & Drachsler, H. (2018). License to evaluate: Preparing learning analytics dashboards for educational practice. Proceedings of the 8th international Conference on learning analytics and knowledge, 31–40.

• Johnson, C. I., & Mayer, R. E. (2010). Applying the self-explanation principle to multimedia learning in a computer-based game-like environment. Computers in Human Behavior, 26(6), 1246–1252.

  Article  Google Scholar 

• Khoshkangini, R., Valetto, G., & Marconi, A. (2017). Generating personalized challenges to enhance the persuasive power of gamification. Personalization in Persuasive Technology Workshop.

• Kickmeier-Rust, M. D., & Albert, D. (2010). Personalized support, guidance, and feedback by embedded assessment and reasoning: What we can learn from educational computer games. IFIP Human-Computer Interaction Symposium, 142–151.

• Koedinger, K. R., Baker, R. S., Cunningham, K., Skogsholm, A., Leber, B., & Stamper, J. (2010). A data repository for the EDM community: The PSLC DataShop. Handbook of Educational Data Mining, 43, 43–56.

  Google Scholar 

• Koedinger, K. R., Brunskill, E., Baker, R. S., McLaughlin, E. A., & Stamper, J. (2013). New potentials for data-driven intelligent tutoring system development and optimization. AI Magazine, 34(3), 27–41.

  Article  Google Scholar 

• Leonardou, A., Rigou, M., & Garofalakis, J. D. (2019). Open learner models in smart learning environments. In Cases on Smart Learning Environments (pp. 346–368). IGI global.

• Li, X., He, S., Dong, Y., Liu, Q., Liu, X., Fu, Y., Shi, Z., & Huang, W. (2010). To create DDA by the approach of ANN from UCT-created data. 2010 international Conference on computer application and system modeling (ICCASM 2010), 8, V8–475.

• Lin, P., Van Brummelen, J., Lukin, G., Williams, R., & Breazeal, C. (2020). Zhorai: Designing a conversational agent for children to explore machine learning concepts. Proceedings of the AAAI Conference on Artificial Intelligence, 34(09), 13381–13388.

  Article  Google Scholar 

• Liu, M., Horton, L., Olmanson, J., & Toprac, P. (2011). A study of learning and motivation in a new media enriched environment for middle school science. Educational Technology Research and Development, 59(2), 249–265.

  Article  Google Scholar 

• Lomas, J. D., Koedinger, K., Patel, N., Shodhan, S., Poonwala, N., & Forlizzi, J. L. (2017). Is difficulty overrated? The effects of choice, novelty and suspense on intrinsic motivation in educational games. In Proceedings of the 2017 CHI conference on human factors in computing systems, 1028–1039.

• Long, Y., & Aleven, V. (2017). Enhancing learning outcomes through self-regulated learning support with an open learner model. User Modeling and User-Adapted Interaction, 27(1), 55–88.

  Article  Google Scholar 

• Long, Y., & Aleven, V. (2016). Mastery-oriented shared student/system control over problem selection in a linear equation tutor. International Conference on intelligent tutoring systems, 90–100.

• Long, Y., & Aleven, V. (2013). Supporting students’ self-regulated learning with an open learner model in a linear equation tutor. International Conference on artificial intelligence in education, 219–228.

• Maass, J. K., Pavlik, P. I., & Hua, H. (2015). How spacing and variable retrieval practice affect the learning of statistics concepts. International Conference on artificial intelligence in education, 247–256.

• Malacria, S., Scarr, J., Cockburn, A., Gutwin, C., & Grossman, T. (2013). Skillometers: Reflective widgets that motivate and help users to improve performance. Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology, 321–330.

• McLaren, B. M., Adams, D. M., Mayer, R. E., & Forlizzi, J. (2017). A computer-based game that promotes mathematics learning more than a conventional approach. International Journal of Game-Based Learning (IJGBL), 7(1), 36–56.

  Article  Google Scholar 

• Mekler, E. D., Bopp, J. A., Tuch, A. N., & Opwis, K. (2014). A systematic review of quantitative studies on the enjoyment of digital entertainment games. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 927–936.

• Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309–326.

  Article  Google Scholar 

• Moreno, R., & Mayer, R. E. (2004). Personalized messages that promote science learning in virtual environments. Journal of Educational Psychology, 96(1), 165–173.

  Article  Google Scholar 

• Mussweiler, T. (2003). Comparison processes in social judgment: Mechanisms and consequences. Psychological Review, 110(3), 472–489.

  Article  Google Scholar 

• Nguyen, H., Harpstead, E., Wang, Y., & McLaren, B. M. (2018). Student agency and game-based learning: A study comparing low and high agency. International Conference on artificial intelligence in education, 338–351.

• Nussbaumer, A., Kravcik, M., Renzel, D., Klamma, R., Berthold, M., & Albert, D. (2014). A framework for facilitating self-regulation in responsive open learning environments. ArXiv Preprint ArXiv:1407.5891.

• Osman, K., & Bakar, N. A. (2012). Educational computer games for Malaysian classrooms: Issues and challenges. Asian Social Science, 8(11), 75.

  Article  Google Scholar 

• Papadimitriou, A., Symeonidis, P., & Manolopoulos, Y. (2012). A generalized taxonomy of explanations styles for traditional and social recommender systems. Data Mining and Knowledge Discovery, 24(3), 555–583.

  Article  Google Scholar 

• Papamitsiou, Z., Economides, A. A., Pappas, I. O., & Giannakos, M. N. (2018). Explaining learning performance using response-time, self-regulation and satisfaction from content: An fsQCA approach. Proceedings of the 8th international Conference on learning analytics and knowledge, 181–190.

• Paquette, L., Baker, R. Sj., Sao Pedro, M. A., Gobert, J. D., Rossi, L., Nakama, A., & Kauffman-Rogoff, Z. (2014). Sensor-free affect detection for a simulation-based science inquiry learning environment. International Conference on intelligent tutoring systems, 1–10.

• Patel, R., Liu, R., & Koedinger, K. R. (2016). When to block versus interleave practice? Evidence against teaching fraction addition before fraction Multiplication. CogSci.

• Peddycord-Liu, Z., Cody, C., Kessler, S., Barnes, T., Lynch, C. F., & Rutherford, T. (2017). Using serious game analytics to inform digital curricular sequencing: What math objective should students play next? Proceedings of the Annual Symposium on Computer-Human Interaction in Play, 195–204.

• Pekrun, R. (2005). Progress and open problems in educational emotion research. Learning and Instruction, 15(5), 497–506.

  Article  Google Scholar 

• Pittman, C. (2013). Teaching with portals: The intersection of video games and physics education. Learning Landscapes, 6(2), 341–360.

  Article  Google Scholar 

• Plass, J. L., O’Keefe, P. A., Homer, B. D., Case, J., Hayward, E. O., Stein, M., & Perlin, K. (2013). The impact of individual, competitive, and collaborative mathematics game play on learning, performance, and motivation. Journal of Educational Psychology, 105(4), 1050–1066.

  Article  Google Scholar 

• Preacher, K. J., & Kelley, K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16(2), 93–115.

  Article  Google Scholar 

• Przybylski, A. K., Rigby, C. S., & Ryan, R. M. (2010). A motivational model of video game engagement. Review of General Psychology, 14(2), 154–166.

  Article  Google Scholar 

• Read, J. C., & MacFarlane, S. (2006). Using the fun toolkit and other survey methods to gather opinions in child computer interaction. Proceedings of the 2006 Conference on Interaction Design and Children, 81–88.

• Read, J. C., MacFarlane, S., & Casey, C. (2002). Endurability, engagement and expectations: Measuring children’s fun. Interaction Design and Children, 2, 1–23.

  Google Scholar 

• Reeve, J., Nix, G., & Hamm, D. (2003). Testing models of the experience of self-determination in intrinsic motivation and the conundrum of choice. Journal of Educational Psychology, 95(2), 375–392.

  Article  Google Scholar 

• Rice, J. W. (2007). New media resistance: Barriers to implementation of computer video games in the classroom. Journal of Educational Multimedia and Hypermedia, 16(3), 249–261.

  Google Scholar 

• Richey, J. E., Zhang, J., Das, R., Andres-Bray, J. M., Scruggs, R., Mogessie, M., Baker, R. S., & McLaren, B. M. (under review). Gaming and confrustion explain learning advantages for a math digital learning game.

• Rohrer, D. (2012). Interleaving helps students distinguish among similar concepts. Educational Psychology Review, 24(3), 355–367.

  Article  MathSciNet  Google Scholar 

• Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.

  Article  Google Scholar 

• Sampayo-Vargas, S., Cope, C. J., He, Z., & Byrne, G. J. (2013). The effectiveness of adaptive difficulty adjustments on students’ motivation and learning in an educational computer game. Computers & Education, 69, 452–462.

  Article  Google Scholar 

• Sarkar, A., & Cooper, S. (2018). Meet your match rating: Providing skill information and choice in player-versus-level matchmaking. Proceedings of the 13th international Conference on the foundations of digital games, 1–8.

• Shute, V., Ke, F., Almond, R. G., Rahimi, S., Smith, G., & Lu, X. (2019). How to increase learning while not decreasing the fun in educational games. Learning Science: Theory, Research, and Practice, 327–357.

• Squire, K. (2005). Changing the game: What happens when video games enter the classroom? Innovate: Journal of Online Education, 1(6).

• Stacey, K., Helme, S., & Steinle, V. (2001). Confusions between decimals, fractions and negative numbers: A consequence of the mirror as a conceptual metaphor in three different ways. PME Conference, 4, 4–217.

  Google Scholar 

• Steinkuehler, C., & Duncan, S. (2008). Scientific habits of mind in virtual worlds. Journal of Science Education and Technology, 17(6), 530–543.

  Article  Google Scholar 

• Tintarev, N., & Masthoff, J. (2011). Designing and evaluating explanations for recommender systems. In Recommender systems handbook (pp. 479–510). Springer.

• Tobias, S., & Fletcher, J. D. (2007). What research has to say about designing computer games for learning. Educational Technology, 20–29.

• Tondello, G. F., & Nacke, L. E. (2019). Player characteristics and video game preferences. Proceedings of the Annual Symposium on Computer-Human Interaction in Play, 365–378.

• Usher, E. L., & Pajares, F. (2008). Self-efficacy for self-regulated learning: A validation study. Educational and Psychological Measurement, 68(3), 443–463.

  Article  MathSciNet  Google Scholar 

• Vallat, R. (2018). Pingouin: Statistics in python. Journal of Open Source Software, 3(31), 1026.

  Article  Google Scholar 

• Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28, 695–704.

  Article  Google Scholar 

• Van Eck, R. (2006). Digital game-based learning: It’s not just the digital natives who are restless. Educause Review, 41(2), 16.

  Google Scholar 

• Vandewaetere, M., & Clarebout, G. (2014). Advanced technologies for personalized learning, instruction, and performance. In Handbook of research on educational communications and technology (pp. 425–437). Springer.

• VanLehn, K. (2016). Regulative loops, step loops and task loops. International Journal of Artificial Intelligence in Education, 26(1), 107–112.

  Article  Google Scholar 

• Wang, Y., Nguyen, H., Harpstead, E., Stamper, J., & McLaren, B. M. (2019). How does order of gameplay impact learning and enjoyment in a digital learning game? International Conference on Artificial Intelligence in Education, 518–531.

• Wardrip-Fruin, N., Mateas, M., Dow, S., & Sali, S. (2009). Agency reconsidered. DiGRA Conference.

• Wechselberger, U. (2013). Learning and enjoyment in serious gaming-contradiction or complement? DiGRA Conference, 26–29.

• Whitley, B. E., & Kite, M. E. (2013). Principles of research in behavioral science. Routledge.

• Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C.-C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 103599.

  Article  Google Scholar 

• Xu, M., Zhai, Y., Guo, Y., Lv, P., Li, Y., Wang, M., & Zhou, B. (2019). Personalized training through Kinect-based games for physical education. Journal of Visual Communication and Image Representation, 62, 394–401.

  Article  Google Scholar 

• Young, M. F., Slota, S., Cutter, A. B., Jalette, G., Mullin, G., Lai, B., Simeoni, Z., Tran, M., & Yukhymenko, M. (2012). Our princess is in another castle: A review of trends in serious gaming for education. Review of Educational Research, 82(1), 61–89.

  Article  Google Scholar 

• Yudelson, M. V., Koedinger, K. R., & Gordon, G. J. (2013). Individualized bayesian knowledge tracing models. International Conference on artificial intelligence in education, 171–180.

• Zagal, J. P., Björk, S., & Lewis, C. (2013). Dark patterns in the design of games.

• Zervas, G., Proserpio, D., & Byers, J. W. (2021). A first look at online reputation on Airbnb, where every stay is above average. Marketing Letters, 32(1), 1–16.

  Article  Google Scholar 

• Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82–91.

  Article  Google Scholar 

• Zohaib, M. (2018). Dynamic difficulty adjustment (DDA) in computer games: A review. Advances in Human-Computer Interaction, 2018, 1–12.

  Article  Google Scholar 

Download references